Algebra for Machine Learning培训

课程编码

algebraforml

课程时长

14 小时 通常来说是2天,包括中间休息。

要求

  • Basic experience or familiarity with machine learning
  • Basic programming experience

课程概览

线性代数是数学的一个分支,处理向量,矩阵和线性变换。线性代数知识可帮助工程师和开发人员提高机器学习能力。理解线性代数概念使他们能够更好地理解机器学习技术背后的原理,从而更快地解决问题。

在这个由讲师指导的实时培训中,参与者将学习线性代数的基础知识,因为他们逐步使用线性代数方法解决机器学习问题。

在培训结束时,参与者将能够:

  • 理解基本的线性代数概念
  • 学习机器学习所需的线性代数技能
  • 在处理数据,图像,算法等时使用线性代数结构和概念。
  • 使用线性代数解决机器学习问题

听众

  • 开发商
  • 工程师

课程形式

  • 部分讲座,部分讨论,练习和繁重的实践练习

注意

  • 要申请本课程的定制培训,请联系我们安排。

Machine Translated

课程大纲

Introduction to Linear Algebra

Why You Should Improve Your Linear Algebra Knowledge for Machine Learning

Learning Linear Algebra Notations

Understanding Vectors

  • Vector Properties and Characteristics
  • Performing Vector Operations

Understanding Matrices

  • Matrix Properties and Characteristics
  • Performing Matrix Operations and Transformations
  • Working with Special Matrices

Solving Linear Systems

  • Representing Problems as Linear Systems
  • Solving Linear Systems

Linear Mappings with Matrices

  • Orthogonal Matrices
  • The Gram-Schmidt Process

Reflecting and Manipulating Images with Matrices

Understanding Eigenvalues and Eigenvectors and their Application to Data Problems

Examining Google's PageRank Algorithm with Eigenvalues and Eigenvectors

Understanding Principal Components Analysis (PCA) for Machine Learning

Understanding Linear Regression for Machine Learning

Project: Solving a Machine Learning Problem with Linear Algebra

Summary and Conclusion

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